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EFEKTIVITAS PELATIHAN POWER BI DALAM MENINGKATKAN LITERASI DATA ADMIN SATU DATA KALIMANTAN BARAT Neva Satyahadewi; Evy Sulistianingsih; Shantika Martha; Nurfitri Imro'ah; Hendra Perdana; Wirda Andani; Ray Tamtama; Yuyun Eka Pratiwi; Muhammad Fikri; Pitriani; Annisa Auliarahmi; Nazwa Nursyifa; Yohanna Gabriel Richsita; Louis Putra Jaya; Jessica Audrey Valeria
Dianmas Bhakti: Jurnal Pengabdian pada Masyarakat Vol 3 No 1 (2026): Dianmas Bhakti: Jurnal Pengabdian pada Masyarakat
Publisher : LPPM Universitas Panca Bhakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54035/dianmas.v3i1.626

Abstract

This Community Service Program (PKM) aimed to enhance data literacy and information visualization skills among Satu Data administrators of local government agencies (OPD) through Microsoft Power BI training at the West Kalimantan Provincial Communication and Information Agency (Diskominfo). The program was implemented through preparation, face-to-face training, and evaluation stages using pre-test and post-test instruments. The training covered fundamental concepts of data analysis, data visualization techniques, and hands-on dashboard development using regional sectoral data. The results of the paired sample t-test analysis indicated a statistically significant improvement between participants’ pre-test and post-test scores, demonstrating the effectiveness of the training. Furthermore, analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that training material quality had a positive and significant effect on participants’ learning outcomes, while other supporting factors such as training duration, facilitator performance, and technical aspects did not show significant effects. These findings highlight that well-structured and relevant training materials play a critical role in improving participants’ competencies. Overall, the program contributed to strengthening analytical skills and supporting the implementation of the Satu Data Indonesia policy toward transparent and evidence-based data governance
K-Means Cluster with Calinski Harabasz Index Evaluation to Map Forest Degradation and Deforestation Areas Ummi Rahimah; Shantika Martha; Nurfitri Imro'ah
Jurnal Matematika UNAND Vol. 15 No. 2 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.2.237-248.2026

Abstract

Although Indonesia is home to a rich biodiversity, the country is threatenedby forest degradation and deforestation, particularly in West Kalimantan. As asignificant contributor to the agricultural sector’s gross domestic product (PDRB), the Sanggau Regency is vital for preserving the environment and promoting sustainable development. This research uses the K-Means Cluster to categorize regions in Sanggau that can potentially experience forest degradation. Then, the Calinski Harabasz Index will be used to determine which clusters are the most effective. Two thousand twentythree, the research findings revealed five ideal clusters, each with a Calinski Harabasz Index value of 3.87. The first cluster consists of one sub-district, the second cluster consists of three sub-districts, the third cluster consists of two sub-districts, the fourth cluster consists of five sub-districts, and the fifth cluster consists of four sub-districts, which are all included in the distribution of clusters. A map illustrating the degree of urgency associated with forest degradation is produced as a result of this study. The map serves as a strategic reference for the government of Sanggau in its efforts to reduce theforest’s degradation and develop areas per the peculiarities of each sub-districts.
Forecasting the Stock Price of PT. Dayamitra Telekomunikasi with Single Input Transfer Function Model Resti Arsanti; Neva Satyahadewi; Shantika Martha
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 2 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss2pp87-96

Abstract

The unpredictable movement of stock prices is often a challenge for investors, so it requires a deeper understanding and consideration of various factors before making investment decisions. One of the factors that affect stock price movements is trading volume. Therefore, this study uses a single input transfer function model to forecast the daily closing stock price of PT. Dayamitra Telekomunikasi, with the closing stock price as the output variable and the stock trading volume as the input variable. The transfer function is a forecasting model that integrates ARIMA with multiple regression analysis, allowing modeling not only based on the values of the output variables, but also considering the influence of the input variables. ARIMA model estimation is performed on the input series for the prewhitening process, then the order of the transfer function is determined using cross-correlation plots, as well as model diagnostic tests to ensure its feasibility. Model accuracy is calculated to evaluate its performance in forecasting. The data used in this study are daily data from the period July 5, 2022 to October 9, 2024. The transfer function model obtained has an order of (2,0,0), with a MAPE value of 1.09%, which indicates that the model has good accuracy. Based on the forecasting results, it is estimated that there will be a decrease in the share price of PT. Dayamitra Telekomunikasi Tbk for the next five periods
Comparison of Single Net Premium of Unit Linked Endowment Life Insurance using Annual Ratchet Method and Black Scholes Model Leona Idilla; Neva Satyahadewi; Shantika Martha
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 2 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss2pp73-86

Abstract

Annual Ratchet is an indexing method. Black Scholes is a model used to determine option. The purpose of this study is to compare the results of single net premium of unit-linked endowment life insurance using the Annual Ratchet method and the Black Scholes Model. The data used in this study are data on the daily closing share price of PT Telkom Indonesia (Persero) Tbk for the period December 20, 2021 to December 20, 2022, Bank Indonesia interest rates and the 2019 Mortality Table. In this study, a comparison is made between the Annual Ratchet method and the Black-Scholes model to calculate the net single premium of unit-linked endowment life insurance for a 30-year-old male insured. The results show that the premium calculated using the Annual Ratchet method is greater than the premium from the Black-Scholes model, which is Rp 8,725,000. This is due to the additional protection feature in the Annual Ratchet method, which provides a minimum guaranteed investment value, thus increasing the premium value to be paid.